(2022 Bolyai Fellowship of the Hungarian Academy of Sciences)
(Hungarian Respiratory Society (MPA #2020))
This study aims to combine computed tomography (CT)-based texture analysis (QTA) and
a microbiome-based biomarker signature to predict the overall survival (OS) of immune
checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by
analyzing their CT scans (n = 129) and fecal microbiome (n = 58). One hundred and
five continuous CT parameters were obtained, where principal component analysis (PCA)
identified seven major components that explained 80% of the data variation. Shotgun
metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial
and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides
distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens
and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales,
Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus
immutabilis and Clavulinopsis fusiformis were more abundant in patients with high
(≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium
were enriched in patients with ICI-related toxicities. An artificial intelligence
(AI) approach based on extreme gradient boosting evaluated the associations between
the outcomes and various clinicopathological parameters. AI identified MG signatures
for patients with a favorable ICI response and high PD-L1 expression, with 84% and
79% accuracy, respectively. The combination of QTA parameters and MG had a positive
predictive value of 90% for both therapeutic response and OS. According to our hypothesis,
the QTA parameters and gut microbiome signatures can predict OS, the response to therapy,
the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine
learning approach can combine these variables to create a reliable predictive model,
as we suggest in this research.
